def get_args_parser()

in projects_oss/detr/main.py [0:0]

• 153 lines of code
• 1 McCabe index (conditional complexity)

def get_args_parser():
    parser = argparse.ArgumentParser("Set transformer detector", add_help=False)
    parser.add_argument("--lr", default=1e-4, type=float)
    parser.add_argument("--lr_backbone", default=1e-5, type=float)
    parser.add_argument("--batch_size", default=2, type=int)
    parser.add_argument("--weight_decay", default=1e-4, type=float)
    parser.add_argument("--epochs", default=300, type=int)
    parser.add_argument("--lr_drop", default=200, type=int)
    parser.add_argument(
        "--clip_max_norm", default=0.1, type=float, help="gradient clipping max norm"
    )

    # Model parameters
    parser.add_argument(
        "--frozen_weights",
        type=str,
        default=None,
        help="Path to the pretrained model. If set, only the mask head will be trained",
    )
    # * Backbone
    parser.add_argument(
        "--backbone",
        default="resnet50",
        type=str,
        help="Name of the convolutional backbone to use",
    )
    parser.add_argument(
        "--dilation",
        action="store_true",
        help="If true, we replace stride with dilation in the last convolutional block (DC5)",
    )
    parser.add_argument(
        "--position_embedding",
        default="sine",
        type=str,
        choices=("sine", "learned"),
        help="Type of positional embedding to use on top of the image features",
    )

    # * Transformer
    parser.add_argument(
        "--enc_layers",
        default=6,
        type=int,
        help="Number of encoding layers in the transformer",
    )
    parser.add_argument(
        "--dec_layers",
        default=6,
        type=int,
        help="Number of decoding layers in the transformer",
    )
    parser.add_argument(
        "--dim_feedforward",
        default=2048,
        type=int,
        help="Intermediate size of the feedforward layers in the transformer blocks",
    )
    parser.add_argument(
        "--hidden_dim",
        default=256,
        type=int,
        help="Size of the embeddings (dimension of the transformer)",
    )
    parser.add_argument(
        "--dropout", default=0.1, type=float, help="Dropout applied in the transformer"
    )
    parser.add_argument(
        "--nheads",
        default=8,
        type=int,
        help="Number of attention heads inside the transformer's attentions",
    )
    parser.add_argument(
        "--num_queries", default=100, type=int, help="Number of query slots"
    )
    parser.add_argument("--pre_norm", action="store_true")

    # * Segmentation
    parser.add_argument(
        "--masks",
        action="store_true",
        help="Train segmentation head if the flag is provided",
    )

    # Loss
    parser.add_argument(
        "--no_aux_loss",
        dest="aux_loss",
        action="store_false",
        help="Disables auxiliary decoding losses (loss at each layer)",
    )
    # * Matcher
    parser.add_argument(
        "--set_cost_class",
        default=1,
        type=float,
        help="Class coefficient in the matching cost",
    )
    parser.add_argument(
        "--set_cost_bbox",
        default=5,
        type=float,
        help="L1 box coefficient in the matching cost",
    )
    parser.add_argument(
        "--set_cost_giou",
        default=2,
        type=float,
        help="giou box coefficient in the matching cost",
    )
    # * Loss coefficients
    parser.add_argument("--mask_loss_coef", default=1, type=float)
    parser.add_argument("--dice_loss_coef", default=1, type=float)
    parser.add_argument("--bbox_loss_coef", default=5, type=float)
    parser.add_argument("--giou_loss_coef", default=2, type=float)
    parser.add_argument(
        "--eos_coef",
        default=0.1,
        type=float,
        help="Relative classification weight of the no-object class",
    )

    # dataset parameters
    parser.add_argument("--dataset_file", default="coco")
    parser.add_argument(
        "--ade_path",
        type=str,
        default="manifold://winvision/tree/detectron2/ADEChallengeData2016/",
    )
    parser.add_argument(
        "--coco_path", type=str, default="manifold://fair_vision_data/tree/"
    )
    parser.add_argument(
        "--coco_panoptic_path", type=str, default="manifold://fair_vision_data/tree/"
    )
    parser.add_argument("--remove_difficult", action="store_true")

    parser.add_argument(
        "--output-dir", default="", help="path where to save, empty for no saving"
    )
    parser.add_argument(
        "--device", default="cuda", help="device to use for training / testing"
    )
    parser.add_argument("--seed", default=42, type=int)
    parser.add_argument("--resume", default="", help="resume from checkpoint")
    parser.add_argument(
        "--start_epoch", default=0, type=int, metavar="N", help="start epoch"
    )
    parser.add_argument("--eval", action="store_true")
    parser.add_argument("--num_workers", default=2, type=int)

    # distributed training parameters
    parser.add_argument(
        "--num-gpus", type=int, default=8, help="number of gpus *per machine*"
    )
    parser.add_argument(
        "--num-machines", type=int, default=1, help="total number of machines"
    )
    parser.add_argument(
        "--machine-rank",
        type=int,
        default=0,
        help="the rank of this machine (unique per machine)",
    )
    parser.add_argument(
        "--dist-url", default="env://", help="url used to set up distributed training"
    )
    return parser